Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes
スポンサーリンク
概要
- 論文の詳細を見る
A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.
- （社）電子情報通信学会の論文
- 2010-09-01
著者
-
Hasegawa Osamu
Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology
-
KAWAWONG Aram
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
-
TANGRUAMSUB Sirinart
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
-
Kawawong Aram
Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology
-
Hasegawa Osamu
Department Of Aquatic Bioscience Graduate School Of Agricultural And Life Sciences
-
Tangruamsub Sirinart
Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology
関連論文
- Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning
- Aneurysmal subarachnoid hemorrhage in a patient with Wegener's granulomatosis
- Clinicopathological analysis of chronic brain stem encephalitis: Comparison with neuro-Behcet's disease
- Photosynthesis-Irradiance Relationship of Phytoplankton and Primary Production in the Vicinity of Kuroshio Warm Core Ring in Spring
- Position-Invariant Robust Features for Long-Term Recognition of Dynamic Outdoor Scenes
- Self-Organizing Incremental Associative Memory-Based Robot Navigation
- Relationship between Coronary Artery Disease and Retinopathy in Patients with Type 2 Diabetes Mellitus